Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory148.6 KiB
Average record size in memory152.1 B

Variable types

Numeric7
Categorical12

Alerts

GamerID is uniformly distributed Uniform
GamerID has unique values Unique
InGamePurchases has 137 (13.7%) zeros Zeros
StreamingHoursPerWeek has 16 (1.6%) zeros Zeros

Reproduction

Analysis started2025-07-10 06:31:33.394601
Analysis finished2025-07-10 06:32:05.785499
Duration32.39 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

GamerID
Real number (ℝ)

Uniform  Unique 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1500.5
Minimum1001
Maximum2000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-07-10T06:32:06.438755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile1050.95
Q11250.75
median1500.5
Q31750.25
95-th percentile1950.05
Maximum2000
Range999
Interquartile range (IQR)499.5

Descriptive statistics

Standard deviation288.81944
Coefficient of variation (CV)0.19248213
Kurtosis-1.2
Mean1500.5
Median Absolute Deviation (MAD)250
Skewness0
Sum1500500
Variance83416.667
MonotonicityStrictly increasing
2025-07-10T06:32:07.039338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2000 1
 
0.1%
1001 1
 
0.1%
1002 1
 
0.1%
1003 1
 
0.1%
1004 1
 
0.1%
1005 1
 
0.1%
1006 1
 
0.1%
1007 1
 
0.1%
1984 1
 
0.1%
1983 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
1001 1
0.1%
1002 1
0.1%
1003 1
0.1%
1004 1
0.1%
1005 1
0.1%
1006 1
0.1%
1007 1
0.1%
1008 1
0.1%
1009 1
0.1%
1010 1
0.1%
ValueCountFrequency (%)
2000 1
0.1%
1999 1
0.1%
1998 1
0.1%
1997 1
0.1%
1996 1
0.1%
1995 1
0.1%
1994 1
0.1%
1993 1
0.1%
1992 1
0.1%
1991 1
0.1%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
0
873 
1
127 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 873
87.3%
1 127
 
12.7%

Length

2025-07-10T06:32:07.484913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-10T06:32:07.731148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 873
87.3%
1 127
 
12.7%

Most occurring characters

ValueCountFrequency (%)
0 873
87.3%
1 127
 
12.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 873
87.3%
1 127
 
12.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 873
87.3%
1 127
 
12.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 873
87.3%
1 127
 
12.7%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
0
865 
1
135 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 865
86.5%
1 135
 
13.5%

Length

2025-07-10T06:32:08.034656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-10T06:32:08.354651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 865
86.5%
1 135
 
13.5%

Most occurring characters

ValueCountFrequency (%)
0 865
86.5%
1 135
 
13.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 865
86.5%
1 135
 
13.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 865
86.5%
1 135
 
13.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 865
86.5%
1 135
 
13.5%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
0
854 
1
146 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 854
85.4%
1 146
 
14.6%

Length

2025-07-10T06:32:08.597814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-10T06:32:09.013695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 854
85.4%
1 146
 
14.6%

Most occurring characters

ValueCountFrequency (%)
0 854
85.4%
1 146
 
14.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 854
85.4%
1 146
 
14.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 854
85.4%
1 146
 
14.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 854
85.4%
1 146
 
14.6%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
0
859 
1
141 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 859
85.9%
1 141
 
14.1%

Length

2025-07-10T06:32:09.462212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-10T06:32:09.898673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 859
85.9%
1 141
 
14.1%

Most occurring characters

ValueCountFrequency (%)
0 859
85.9%
1 141
 
14.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 859
85.9%
1 141
 
14.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 859
85.9%
1 141
 
14.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 859
85.9%
1 141
 
14.1%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
0
852 
1
148 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 852
85.2%
1 148
 
14.8%

Length

2025-07-10T06:32:10.471397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-10T06:32:10.861460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 852
85.2%
1 148
 
14.8%

Most occurring characters

ValueCountFrequency (%)
0 852
85.2%
1 148
 
14.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 852
85.2%
1 148
 
14.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 852
85.2%
1 148
 
14.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 852
85.2%
1 148
 
14.8%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
0
858 
1
142 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 858
85.8%
1 142
 
14.2%

Length

2025-07-10T06:32:11.718596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-10T06:32:12.310936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 858
85.8%
1 142
 
14.2%

Most occurring characters

ValueCountFrequency (%)
0 858
85.8%
1 142
 
14.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 858
85.8%
1 142
 
14.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 858
85.8%
1 142
 
14.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 858
85.8%
1 142
 
14.2%

AvgSessionTimeMins
Real number (ℝ)

Distinct614
Distinct (%)61.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.3999
Minimum10
Maximum158.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-07-10T06:32:13.003568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10.795
Q138.875
median60.45
Q382.725
95-th percentile110.285
Maximum158.3
Range148.3
Interquartile range (IQR)43.85

Descriptive statistics

Standard deviation29.571291
Coefficient of variation (CV)0.4816179
Kurtosis-0.37170915
Mean61.3999
Median Absolute Deviation (MAD)22.1
Skewness0.24536571
Sum61399.9
Variance874.46123
MonotonicityNot monotonic
2025-07-10T06:32:13.358266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 47
 
4.7%
66 6
 
0.6%
81.1 5
 
0.5%
47 4
 
0.4%
86.1 4
 
0.4%
53.7 4
 
0.4%
87.7 4
 
0.4%
61.6 4
 
0.4%
54.8 4
 
0.4%
68.2 4
 
0.4%
Other values (604) 914
91.4%
ValueCountFrequency (%)
10 47
4.7%
10.3 1
 
0.1%
10.4 1
 
0.1%
10.7 1
 
0.1%
10.8 1
 
0.1%
11.2 1
 
0.1%
11.6 1
 
0.1%
12.1 1
 
0.1%
12.8 1
 
0.1%
12.9 1
 
0.1%
ValueCountFrequency (%)
158.3 1
0.1%
152.5 1
0.1%
149.3 1
0.1%
145.6 1
0.1%
144.2 1
0.1%
137.4 1
0.1%
133.8 2
0.2%
132 2
0.2%
131.8 1
0.1%
129.5 1
0.1%

SessionsPerWeek
Real number (ℝ)

Distinct14
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.481
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-07-10T06:32:15.208857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median8
Q311
95-th percentile14
Maximum14
Range13
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.0316117
Coefficient of variation (CV)0.53891347
Kurtosis-1.2141937
Mean7.481
Median Absolute Deviation (MAD)3.5
Skewness-0.015293938
Sum7481
Variance16.253893
MonotonicityNot monotonic
2025-07-10T06:32:15.539773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
9 85
 
8.5%
2 84
 
8.4%
10 74
 
7.4%
7 73
 
7.3%
3 72
 
7.2%
13 72
 
7.2%
11 71
 
7.1%
8 71
 
7.1%
4 69
 
6.9%
14 69
 
6.9%
Other values (4) 260
26.0%
ValueCountFrequency (%)
1 68
6.8%
2 84
8.4%
3 72
7.2%
4 69
6.9%
5 62
6.2%
6 64
6.4%
7 73
7.3%
8 71
7.1%
9 85
8.5%
10 74
7.4%
ValueCountFrequency (%)
14 69
6.9%
13 72
7.2%
12 66
6.6%
11 71
7.1%
10 74
7.4%
9 85
8.5%
8 71
7.1%
7 73
7.3%
6 64
6.4%
5 62
6.2%

InGamePurchases
Real number (ℝ)

Zeros 

Distinct9
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.004
Minimum0
Maximum8
Zeros137
Zeros (%)13.7%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-07-10T06:32:15.887007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum8
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4275931
Coefficient of variation (CV)0.7123718
Kurtosis0.63001872
Mean2.004
Median Absolute Deviation (MAD)1
Skewness0.74172986
Sum2004
Variance2.038022
MonotonicityNot monotonic
2025-07-10T06:32:16.303752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2 280
28.0%
1 266
26.6%
3 168
16.8%
0 137
13.7%
4 93
 
9.3%
5 44
 
4.4%
7 6
 
0.6%
6 4
 
0.4%
8 2
 
0.2%
ValueCountFrequency (%)
0 137
13.7%
1 266
26.6%
2 280
28.0%
3 168
16.8%
4 93
 
9.3%
5 44
 
4.4%
6 4
 
0.4%
7 6
 
0.6%
8 2
 
0.2%
ValueCountFrequency (%)
8 2
 
0.2%
7 6
 
0.6%
6 4
 
0.4%
5 44
 
4.4%
4 93
 
9.3%
3 168
16.8%
2 280
28.0%
1 266
26.6%
0 137
13.7%

AchievementsUnlocked
Real number (ℝ)

Distinct100
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.1
Minimum0
Maximum99
Zeros10
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-07-10T06:32:16.535444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q124
median49
Q373
95-th percentile93
Maximum99
Range99
Interquartile range (IQR)49

Descriptive statistics

Standard deviation28.771928
Coefficient of variation (CV)0.58598631
Kurtosis-1.1829423
Mean49.1
Median Absolute Deviation (MAD)24.5
Skewness-0.017341363
Sum49100
Variance827.82382
MonotonicityNot monotonic
2025-07-10T06:32:17.065142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48 19
 
1.9%
66 17
 
1.7%
33 17
 
1.7%
62 17
 
1.7%
83 16
 
1.6%
86 15
 
1.5%
27 15
 
1.5%
56 15
 
1.5%
15 15
 
1.5%
46 14
 
1.4%
Other values (90) 840
84.0%
ValueCountFrequency (%)
0 10
1.0%
1 11
1.1%
2 14
1.4%
3 8
0.8%
4 13
1.3%
5 11
1.1%
6 5
 
0.5%
7 14
1.4%
8 14
1.4%
9 7
0.7%
ValueCountFrequency (%)
99 13
1.3%
98 9
0.9%
97 7
0.7%
96 9
0.9%
95 6
0.6%
94 5
 
0.5%
93 13
1.3%
92 8
0.8%
91 13
1.3%
90 8
0.8%

FriendsInNetwork
Real number (ℝ)

Distinct293
Distinct (%)29.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean148.651
Minimum0
Maximum299
Zeros2
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-07-10T06:32:17.561090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15.95
Q172.75
median147
Q3223
95-th percentile285
Maximum299
Range299
Interquartile range (IQR)150.25

Descriptive statistics

Standard deviation87.648796
Coefficient of variation (CV)0.58962803
Kurtosis-1.234173
Mean148.651
Median Absolute Deviation (MAD)75
Skewness0.028109448
Sum148651
Variance7682.3115
MonotonicityNot monotonic
2025-07-10T06:32:17.919336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
168 9
 
0.9%
37 9
 
0.9%
286 9
 
0.9%
260 8
 
0.8%
285 8
 
0.8%
169 8
 
0.8%
150 7
 
0.7%
21 7
 
0.7%
95 7
 
0.7%
268 7
 
0.7%
Other values (283) 921
92.1%
ValueCountFrequency (%)
0 2
 
0.2%
1 2
 
0.2%
2 2
 
0.2%
3 2
 
0.2%
5 3
0.3%
6 2
 
0.2%
7 4
0.4%
8 5
0.5%
9 4
0.4%
10 5
0.5%
ValueCountFrequency (%)
299 1
 
0.1%
298 3
0.3%
297 3
0.3%
296 6
0.6%
295 3
0.3%
294 2
 
0.2%
293 2
 
0.2%
292 3
0.3%
291 4
0.4%
290 3
0.3%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
1
688 
0
312 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 688
68.8%
0 312
31.2%

Length

2025-07-10T06:32:18.412723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-10T06:32:18.664010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 688
68.8%
0 312
31.2%

Most occurring characters

ValueCountFrequency (%)
1 688
68.8%
0 312
31.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 688
68.8%
0 312
31.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 688
68.8%
0 312
31.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 688
68.8%
0 312
31.2%

UsesVoiceChat
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
1
522 
0
478 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 522
52.2%
0 478
47.8%

Length

2025-07-10T06:32:18.981824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-10T06:32:19.201088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 522
52.2%
0 478
47.8%

Most occurring characters

ValueCountFrequency (%)
1 522
52.2%
0 478
47.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 522
52.2%
0 478
47.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 522
52.2%
0 478
47.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 522
52.2%
0 478
47.8%

StreamingHoursPerWeek
Real number (ℝ)

Zeros 

Distinct124
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8888
Minimum0
Maximum25.2
Zeros16
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-07-10T06:32:19.378767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q10.8
median1.9
Q33.9
95-th percentile8.8
Maximum25.2
Range25.2
Interquartile range (IQR)3.1

Descriptive statistics

Standard deviation3.0545613
Coefficient of variation (CV)1.0573807
Kurtosis7.9037788
Mean2.8888
Median Absolute Deviation (MAD)1.3
Skewness2.3318911
Sum2888.8
Variance9.3303449
MonotonicityNot monotonic
2025-07-10T06:32:19.820743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 39
 
3.9%
0.5 38
 
3.8%
1 34
 
3.4%
0.1 32
 
3.2%
0.8 31
 
3.1%
1.1 31
 
3.1%
0.4 30
 
3.0%
0.6 29
 
2.9%
2.4 26
 
2.6%
0.9 25
 
2.5%
Other values (114) 685
68.5%
ValueCountFrequency (%)
0 16
1.6%
0.1 32
3.2%
0.2 39
3.9%
0.3 20
2.0%
0.4 30
3.0%
0.5 38
3.8%
0.6 29
2.9%
0.7 20
2.0%
0.8 31
3.1%
0.9 25
2.5%
ValueCountFrequency (%)
25.2 1
0.1%
21.5 1
0.1%
18.9 1
0.1%
18.6 1
0.1%
17.8 1
0.1%
15.6 2
0.2%
15.2 1
0.1%
14.9 1
0.1%
14.7 1
0.1%
14.4 1
0.1%

GamerType_Casual
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
0
816 
1
184 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 816
81.6%
1 184
 
18.4%

Length

2025-07-10T06:32:20.294067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-10T06:32:20.579351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 816
81.6%
1 184
 
18.4%

Most occurring characters

ValueCountFrequency (%)
0 816
81.6%
1 184
 
18.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 816
81.6%
1 184
 
18.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 816
81.6%
1 184
 
18.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 816
81.6%
1 184
 
18.4%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
0
778 
1
222 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 778
77.8%
1 222
 
22.2%

Length

2025-07-10T06:32:20.804783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-10T06:32:20.914636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 778
77.8%
1 222
 
22.2%

Most occurring characters

ValueCountFrequency (%)
0 778
77.8%
1 222
 
22.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 778
77.8%
1 222
 
22.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 778
77.8%
1 222
 
22.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 778
77.8%
1 222
 
22.2%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
0
800 
1
200 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 800
80.0%
1 200
 
20.0%

Length

2025-07-10T06:32:21.056327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-10T06:32:21.426779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 800
80.0%
1 200
 
20.0%

Most occurring characters

ValueCountFrequency (%)
0 800
80.0%
1 200
 
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 800
80.0%
1 200
 
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 800
80.0%
1 200
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 800
80.0%
1 200
 
20.0%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
0
816 
1
184 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 816
81.6%
1 184
 
18.4%

Length

2025-07-10T06:32:21.812126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-10T06:32:22.058822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 816
81.6%
1 184
 
18.4%

Most occurring characters

ValueCountFrequency (%)
0 816
81.6%
1 184
 
18.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 816
81.6%
1 184
 
18.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 816
81.6%
1 184
 
18.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 816
81.6%
1 184
 
18.4%

Interactions

2025-07-10T06:32:00.700046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T06:31:35.983277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T06:31:42.386442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T06:31:47.629462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T06:31:50.688187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T06:31:53.958873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T06:31:57.055017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T06:32:01.351733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T06:31:36.393467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T06:31:43.203885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T06:31:47.930363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T06:31:50.998570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T06:31:54.132052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T06:31:57.628293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T06:32:01.751832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T06:31:36.881165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T06:31:44.098112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T06:31:48.330701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T06:31:51.587035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T06:31:54.488539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T06:31:58.189158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T06:32:02.281712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T06:31:37.285422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T06:31:45.510599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T06:31:48.752401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T06:31:52.232666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T06:31:54.948372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T06:31:58.936141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T06:32:02.818294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T06:31:37.614132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T06:31:46.082940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T06:31:49.057420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T06:31:52.636231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T06:31:55.504672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T06:31:59.534155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T06:32:03.168875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T06:31:38.042686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T06:31:46.577035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T06:31:49.592228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T06:31:53.095058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T06:31:55.942306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T06:31:59.741180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T06:32:03.555792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T06:31:38.442407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T06:31:47.290744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T06:31:50.122467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T06:31:53.510490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T06:31:56.408930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T06:32:00.193227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-07-10T06:32:22.504207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AchievementsUnlockedAvgSessionTimeMinsFriendsInNetworkGamerIDGamerType_CasualGamerType_ExplorerGamerType_SocializerGamerType_StrategistInGamePurchasesPreferredGenre_AdventurePreferredGenre_PuzzlePreferredGenre_RPGPreferredGenre_ShooterPreferredGenre_SimulationPreferredGenre_SportsPrefersMultiplayerSessionsPerWeekStreamingHoursPerWeekUsesVoiceChat
AchievementsUnlocked1.000-0.0020.018-0.0340.0000.0000.0380.000-0.0180.0060.0230.0000.0390.0000.0000.000-0.0010.0080.028
AvgSessionTimeMins-0.0021.0000.079-0.0370.0320.0560.0000.0790.0450.0000.0430.0000.0000.0540.0000.0000.0140.0250.000
FriendsInNetwork0.0180.0791.000-0.0060.0000.0230.1030.0000.0270.0000.0000.0310.0000.0000.0200.000-0.003-0.0070.000
GamerID-0.034-0.037-0.0061.0000.0000.0520.0270.0300.0330.0000.0410.0000.0000.0000.0000.000-0.014-0.0210.045
GamerType_Casual0.0000.0320.0000.0001.0000.2200.2320.2490.0420.0000.0000.0000.0000.0460.0000.0000.0440.0000.000
GamerType_Explorer0.0000.0560.0230.0520.2201.0000.2320.2490.0640.0000.0360.0370.0000.0000.0000.0000.0850.0000.000
GamerType_Socializer0.0380.0000.1030.0270.2320.2321.0000.2620.0000.0000.0000.0440.0000.0000.0000.0000.0550.0000.000
GamerType_Strategist0.0000.0790.0000.0300.2490.2490.2621.0000.0480.0000.0000.0000.0000.0320.0000.0000.0450.0000.000
InGamePurchases-0.0180.0450.0270.0330.0420.0640.0000.0481.0000.1330.0000.0000.0220.0000.0000.0450.0030.0560.000
PreferredGenre_Adventure0.0060.0000.0000.0000.0000.0000.0000.0000.1331.0000.1430.1500.1470.1520.1480.0000.0000.0000.000
PreferredGenre_Puzzle0.0230.0430.0000.0410.0000.0360.0000.0000.0000.1431.0000.1560.1530.1570.1530.0120.0000.0000.016
PreferredGenre_RPG0.0000.0000.0310.0000.0000.0370.0440.0000.0000.1500.1561.0000.1600.1650.1610.0000.0000.0080.000
PreferredGenre_Shooter0.0390.0000.0000.0000.0000.0000.0000.0000.0220.1470.1530.1601.0000.1620.1580.0000.0000.0000.000
PreferredGenre_Simulation0.0000.0540.0000.0000.0460.0000.0000.0320.0000.1520.1570.1650.1621.0000.1630.0000.0360.0680.000
PreferredGenre_Sports0.0000.0000.0200.0000.0000.0000.0000.0000.0000.1480.1530.1610.1580.1631.0000.0000.0000.0150.000
PrefersMultiplayer0.0000.0000.0000.0000.0000.0000.0000.0000.0450.0000.0120.0000.0000.0000.0001.0000.0140.1350.000
SessionsPerWeek-0.0010.014-0.003-0.0140.0440.0850.0550.0450.0030.0000.0000.0000.0000.0360.0000.0141.000-0.0640.067
StreamingHoursPerWeek0.0080.025-0.007-0.0210.0000.0000.0000.0000.0560.0000.0000.0080.0000.0680.0150.135-0.0641.0000.035
UsesVoiceChat0.0280.0000.0000.0450.0000.0000.0000.0000.0000.0000.0160.0000.0000.0000.0000.0000.0670.0351.000

Missing values

2025-07-10T06:32:04.377795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-10T06:32:05.044523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

GamerIDPreferredGenre_AdventurePreferredGenre_PuzzlePreferredGenre_RPGPreferredGenre_ShooterPreferredGenre_SimulationPreferredGenre_SportsAvgSessionTimeMinsSessionsPerWeekInGamePurchasesAchievementsUnlockedFriendsInNetworkPrefersMultiplayerUsesVoiceChatStreamingHoursPerWeekGamerType_CasualGamerType_StrategistGamerType_SocializerGamerType_Explorer
01001000000108.4506967100.30000
1100201000043.211236210110.70010
2100300010054.8628241110.90000
3100400010086.0437212110.31000
4100500000066.2306212108.11000
5100600010054.84257141113.80001
61007000001129.08210269110.10001
7100800000038.09487187105.51000
8100900001022.38036253104.90001
9101000100044.3625324101.10001
GamerIDPreferredGenre_AdventurePreferredGenre_PuzzlePreferredGenre_RPGPreferredGenre_ShooterPreferredGenre_SimulationPreferredGenre_SportsAvgSessionTimeMinsSessionsPerWeekInGamePurchasesAchievementsUnlockedFriendsInNetworkPrefersMultiplayerUsesVoiceChatStreamingHoursPerWeekGamerType_CasualGamerType_StrategistGamerType_SocializerGamerType_Explorer
990199100000070.610572780114.70000
991199210000029.45727263106.00100
992199300000176.5313519001.20010
993199400000123.6319820113.21000
994199500001036.510117239111.50010
995199610000028.38159167112.80010
996199700100088.110326127101.91000
997199810000085.7961567110.70001
998199900010038.2133598102.00010
999200000010061.05490254107.21000